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Journal articles on the topic 'Learning vector quantization neural network'

Author: Grafiati
Published: 3 June 2025
Last updated: 7 July 2025

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1

Andriani, Siska, and Kotim Subandi. "Weather Forecast using Learning Vector Quantization Methods." Procedia of Social Sciences and Humanities 1 (March 2, 2021): 69–74. http://dx.doi.org/10.21070/pssh.v1i.22.

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Weather forecasting is one of the important factors in daily life, as it can affect the activities carried out by the community. The study was conducted to optimize weather forecasts using artificial neural network methods. The artificial neural network used is a learning vector quantization (LVQ) method, in which artificial neural networks based on previous research are suitable for prediction. The research is modeling weather forecast optimization using the LVQ method. Models with the best accuracy can be used in terms of weather forecasts. Based on the results of the training that has been done in this study produces the best accuracy on the LVQ method which is to produce 72%.
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Begum, Afsana, Md Masiur Rahman, and Sohana Jahan. "Medical diagnosis using artificial neural networks." Mathematics in Applied Sciences and Engineering 5, no. 2 (2024): 149–64. http://dx.doi.org/10.5206/mase/17138.

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Medical diagnosis using Artificial Neural Networks (ANN) and computer-aided diagnosis with deep learning is currently a very active research area in medical science. In recent years, for medical diagnosis, neural network models are broadly considered since they are ideal for recognizing different kinds of diseases including autism, cancer, tumor lung infection, etc. It is evident that early diagnosis of any disease is vital for successful treatment and improved survival rates. In this research, five neural networks, Multilayer neural network (MLNN), Probabilistic neural network (PNN), Learning vector quantization neural network (LVQNN), Generalized regression neural network (GRNN), and Radial basis function neural network (RBFNN) have been explored. These networks are applied to several benchmarking data collected from the University of California Irvine (UCI) Machine Learning Repository. Results from numerical experiments indicate that each network excels at recognizing specific physical issues. In the majority of cases, both the Learning Vector Quantization Neural Network and the Probabilistic Neural Network demonstrate superior performance compared to the other networks.
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Burrascano, P. "Learning vector quantization for the probabilistic neural network." IEEE Transactions on Neural Networks 2, no. 4 (1991): 458–61. http://dx.doi.org/10.1109/72.88165.

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4

Kozukue, Wakae, and Hideyuki Miyaji. "DEFECT IDENTIFICATION USING LEARNING VECTOR QUANTIZATION NEURAL NETWORK." Proceedings of the International Conference on Motion and Vibration Control 6.2 (2002): 1181–84. http://dx.doi.org/10.1299/jsmeintmovic.6.2.1181.

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5

YAN, HONG. "CONSTRAINED LEARNING VECTOR QUANTIZATION." International Journal of Neural Systems 05, no. 02 (1994): 143–52. http://dx.doi.org/10.1142/s0129065794000165.

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Kohonen’s learning vector quantization (LVQ) is an efficient neural network based technique for pattern recognition. The performance of the method depends on proper selection of the learning parameters. Over-training may cause a degradation in recognition rate of the final classifier. In this paper we introduce constrained learning vector quantization (CLVQ). In this method the updated coefficients in each iteration are accepted only if the recognition performance of the classifier after updating is not decreased for the training samples compared with that before updating, a constraint widely used in many prototype editing procedures to simplify and optimize a nearest neighbor classifier (NNC). An efficient computer algorithm is developed to implement this constraint. The method is verified with experimental results. It is shown that CLVQ outperforms and may even require much less training time than LVQ.
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Soflaei, Masoumeh, Hongyu Guo, Ali Al-Bashabsheh, Yongyi Mao, and Richong Zhang. "Aggregated Learning: A Vector-Quantization Approach to Learning Neural Network Classifiers." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (2020): 5810–17. http://dx.doi.org/10.1609/aaai.v34i04.6038.

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We consider the problem of learning a neural network classifier. Under the information bottleneck (IB) principle, we associate with this classification problem a representation learning problem, which we call “IB learning”. We show that IB learning is, in fact, equivalent to a special class of the quantization problem. The classical results in rate-distortion theory then suggest that IB learning can benefit from a “vector quantization” approach, namely, simultaneously learning the representations of multiple input objects. Such an approach assisted with some variational techniques, result in a novel learning framework, “Aggregated Learning”, for classification with neural network models. In this framework, several objects are jointly classified by a single neural network. The effectiveness of this framework is verified through extensive experiments on standard image recognition and text classification tasks.
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Pham, D. T., and E. J. Bayro-Corrochano. "Neural Classifiers for Automated Visual Inspection." Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering 208, no. 2 (1994): 83–89. http://dx.doi.org/10.1243/pime_proc_1994_208_166_02.

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This paper discusses the application of a back-propagation multi-layer perceptron and a learning vector quantization network to the classification of defects in valve stem seals for car engines. Both networks were trained with vectors containing descriptive attributes of known flaws. These attribute vectors (‘signatures’) were extracted from images of the seals captured by an industrial vision system. The paper describes the hardware and techniques used and the results obtained.
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Yang, Degang, Guo Chen, Hui Wang, and Xiaofeng Liao. "Learning vector quantization neural network method for network intrusion detection." Wuhan University Journal of Natural Sciences 12, no. 1 (2007): 147–50. http://dx.doi.org/10.1007/s11859-006-0258-z.

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Ding, Shuo, Xiao Heng Chang, and Qing Hui Wu. "A Study on the Application of Learning Vector Quantization Neural Network in Pattern Classification." Applied Mechanics and Materials 525 (February 2014): 657–60. http://dx.doi.org/10.4028/www.scientific.net/amm.525.657.

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Standard back propagation (BP) neural network has disadvantages such as slow convergence speed, local minimum and difficulty in definition of network structure. In this paper, an learning vector quantization (LVQ) neural network classifier is established, then it is applied in pattern classification of two-dimensional vectors on a plane. To test its classification ability, the classification results of LVQ neural network and BP neural network are compared with each other. The simulation result shows that compared with classification method based on BP neural network, the one based on LVQ neural network has a shorter learning time. Besides, its requirements for learning samples and the number of competing layers are also lower. Therefore it is an effective classification method which is powerful in classification of two-dimensional vectors on a plane.
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Abdulmuhsin, Kamel A., and Iftekhar A. Al-Ani. "Using of Learning Vector Quantization Network for Pan Evaporation Estimation." Tikrit Journal of Engineering Sciences 16, no. 2 (2009): 43–50. http://dx.doi.org/10.25130/tjes.16.2.07.

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A modern technique is presented to study the evaporation process which is considered as an important component of the hydrological cycle. The Pan Evaporation depth is estimated depending upon four metrological factors viz. (temperature, relative humidity, sunshine, and wind speed). Unsupervised Artificial Neural Network has been proposed to accomplish the study goal, specifically, a type called Linear Vector Quantitization, (LVQ). A step by step method is used to cope with difficulties that usually associated with computation procedures inherent in these kind of networks. Such systematic approach may close the gap between the hesitation of the user to make use of the capabilities of these type of neural networks and the relative complexity involving the computations procedures. The results reveal the possibility of using LVQ for of Pan Evaporation depth estimation where a good agreement has been noticed between the outputs of the proposed network and the observed values of the Pan Evaporation depth with a correlation coefficient of 0.986.
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Muhammad Varriel Avenazh Nizar, Sirajuddin Hawari, and Ahmad Nur Ihsan Purwanto. "MEMBANDINGAN METODE JARINGAN SYARAF TIRUAN BACKPROPAGATION DAN LEARNING VECTOR QUANTIZATION DENGAN OPENCV PADA PENGENALAN WAJAH." JURAL RISET RUMPUN ILMU TEKNIK 1, no. 1 (2022): 107–14. http://dx.doi.org/10.55606/jurritek.v1i1.593.

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Face recognition is an area that is still being researched and improved for various purposes such as attendance, population data collection, security systems and others. Two methods that are often used for face recognition applications are artificial intelligence methods, especially back-propagation neural networks (ANN) and learning vector quantization. Both of these techniques are directed learning techniques that are widely used to identify distinctive patterns, namely grouping patterns into groups of patterns, making them ideal for use in facial recognition applications. In this application, preprocessing of the input image includes the detection process of scaling, grayscale, edged with the sobel and threshold methods, carried out before the image is processed in ANN. Meanwhile, the ANN approach used to identify faces involves the Backpropagation method and the Learning Vector Quantization method. The findings of this analysis are a comparison of the backpropagation neural network method and quantization of the learning vectors of face recognition used to assess variations, limitations, strengths and optimal results of the two techniques for use in facial recognition systems.
 
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Arif, M. Nurul, Misbah Misbah, and Yoedo Ageng Surya. "Klasifikasi Aroma Tembakau Menggunakan Learning Vector Quantization." E-Link : Jurnal Teknik Elektro dan Informatika 14, no. 2 (2020): 43. http://dx.doi.org/10.30587/e-link.v14i2.1198.

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Aroma tembakau ditentukan oleh kandungan gas-gas atau jumlah campuran bahan organik yang mudah menguap dan tidak mudah menguap. Proses penentuan sebelumnya telah di lakukan dengan metode analistis konvensional,yang melibatkan kombinasi antara manusia dan instrumentasi sekala besar. Metode ini sangat mahal dalam kaitannya dengan waktu dan tenaga kerja, karena membutuhkan peralatan yang sangat komplek dan tingkat ketelitian dari analisa yang di lakukan oleh ahli tembakau pada saat tertentu, karena indra penciuman ahli tembakau menjadi sangat rendah pada saat tertentu. karena indra penciuman manusia sangat tergantung pada kelembaban, suhu dan kondisi fisik. Oleh sebab itu di buatlah alat yang dapat mendekati dari hasil penciuman para ahli tembakau. Dengan mengalirkan gas yang di hasilkan tembakau ke sensor untuk dideteksi dan di lakukan proses sinyal analog menjadi sinyal digital (ADC). Setelah proses ADC, data akan di kirim ke pc melalui komunikasi serial untuk di lakukan proses pelatihan neural network menggunakan learning vector quantization untuk menentukan bobot dari jaringan neural network kemudian hasil dari pelatihan digunakan untuk klasifikasi tembakau yang diterima dan ditolak. Dari hasil pengujian sistem ini dapat mengidentifikasi tembakau yang diterima dan tembakau yang ditolak dengan tingkat ke akuratan 93,3%.
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Gea, Junita. "Implementasi Algoritma Learning Vector Quantization Untuk Pengenalan Barcode Barang." Journal of Informatics, Electrical and Electronics Engineering 2, no. 1 (2022): 1–4. http://dx.doi.org/10.47065/jieee.v2i1.385.

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Problems in barcode recognition during the barcode identification process. Where when the barcode has noise (damage) then the barcode becomes difficult to recognize. Learning Vector Quantization (LVQ) is a classification method in which each output unit presents a class. LVQ is used for grouping and is also one of the artificial neural networks which is a competitive learning algorithm supervised version of the Kohonen Self-Organizing Map (SOM) algorithm. The purpose of this algorithm is to approach the distribution of vector classes in order to minimize errors in classifying. LVQ learning models are trained significantly to be faster than other algorithms such as the Back Propagation Neural Network. This can summarize or reduce large datasets for a small number of vectors. Based on the results of barcode recognition testing using LVQ algorithm success with training data as much as 4 and conducted calrifikas trial of two data namely: {1,1,1,0} and {1,0,1,1}. Obtained accuracy value generated as much as 90% barcode recognized. The more training data used, LVQ will have a more complete knowledge.
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Yang, Jian, Chun Yan Xia, Xiu Ying Li, He Pan, and Ying Shi. "Research on Recognition of Fertilized Egg Based on Optimized LVQ by Genetic Algorithm." Applied Mechanics and Materials 472 (January 2014): 522–26. http://dx.doi.org/10.4028/www.scientific.net/amm.472.522.

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In order to detect fertilized eggs nondestructively to improve hatching rate, this paper uses the method of image processing and Learning Vector Quantization neural network to identify fertilized eggs. Firstly, we use image collection device to collect images of the unfertilized and fertilized eggs and extract the feature of egg image, and then determine 5 principal component characteristics of the egg shape. Learning vector quantization neural networkis 5 dimensional input and 1 dimensional outputs.Finally,we use Genetic Algorithm to optimize the weights and threshold of neural network, which can be used to predict the condition of fertilization. The experiment shows that, compared with the traditional LVQ neural network, it is more accurate to recognize the fertilized eggs when using optimized LVQ neural network by genetic algorithm. The rate can reach 98.21%, which meets therequirements of recognizing fertilized eggs.
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Linawati, Sudarma Made, and Putu Oka Wisnawa I. "Forecasting rupiah exchange rate with learning vector quantization neural network." Indonesian Journal of Electrical Engineering and Computer Science (IJEECS) 18, no. 1 (2020): 24–31. https://doi.org/10.11591/ijeecs.v18.i1.pp24-31.

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The classification technique and data forecasting will probably be one of the techniques that will often be needed in handling or managing big data. So, from that the author analyzes the possible development of the existing algorithms. The purpose is to find possibilities in the use of reliable algorithms in a particular field, then can be adopted and implemented to develop forecasting techniques in the future. Based on these considerations, the authors conducted experiments by applying LVQNN to conduct shortterm forecasting on daily period of the Rupiah exchange rate. The literature that is used as a reference is the discovery of architectural data classification processes that resemble forecasting techniques. So, when there is a combination of Rupiah exchange histories, it is possible to find these combinations into certain classes based on predetermined parameters and historical data combination data and forecast values in the past. In this research the factors chosen as indicators that affect the Rupiah exchange rate are the amount of exports, the amount of imports, the inflation rate and also the world oil price. In this research the highest accuracy value in the testing process for the population reached 99.0991%. The increase in the percentage value of forecasting accuracy is influenced by the composition of the data. In this study the formation of data composition is influenced by distinct data. The selection of parameters which become distinct claused determines how the composition of the data will be formed. If the composition of the data is not correct, the test results will not be good. If the number of weights vector is smaller than the input data, the forecasting accuracy will decrease. Because the weight vector cannot represent data combinations that used during training or testing.
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DR., (MRS.) S. S. LOKHANDE. "MISSING SENSOR IDENTIFICATION USING NEURAL NETWORK IN CONCRETE STRUCTURE." IJIERT - International Journal of Innovations in Engineering Research and Technology ICITER- 16 PUNE (June 20, 2016): 92–95. https://doi.org/10.5281/zenodo.1463565.

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<strong>It is crucial to quantify location of sensor in the dam,for this purport numbers of the sensor are placed at various locations in the dam. Central water and Power Research Station (CWPRS) provided data of Dam sensor which utilized in this project to train the Neural Network. Identification of sensor location is the arduous task if the quandary is arrived in the control room,because of Mismatching in the wire,Noise. Learning Vector Quantization method used in this project to train the network.Estimating the value s of dam sensor using the previous data of same dam sensor.</strong> <strong>https://www.ijiert.org/paper-details?paper_id=140889</strong>
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Damarla, Seshu K., and Biao Huang. "Control Valve Stiction Detection using Learning Vector Quantization Neural Network." IFAC-PapersOnLine 58, no. 14 (2024): 379–83. http://dx.doi.org/10.1016/j.ifacol.2024.08.366.

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KOZUKUE, Wakae, and Hideyuki MIYAJI. "F-0629 Structural Identification Using Learning Vector Quantization Neural Network." Proceedings of the JSME annual meeting VI.01.1 (2001): 51–52. http://dx.doi.org/10.1299/jsmemecjo.vi.01.1.0_51.

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19

Abdul Hameed, W., Raja Das, and Jitendra Jaiswal. "Breast Cancer Prognosis Using Learning Vector Quantization Neural Network Technique." International Journal of Engineering & Technology 7, no. 4.10 (2018): 922. http://dx.doi.org/10.14419/ijet.v7i4.10.26789.

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A suitable treatment coming after surgery is very much motivated by prognosis - the speculated outcome of the disease. Now-a-days improving prognostic prediction is a challenging task to the doctors. This paper presents prognosis for the breast cancer issues by applying Neural Network Architecture with the dataset for Wisconsin Prognostic Breast Cancer. The accuracy is evaluated by adopting algorithm for Kohonen’s first issue of Learning Vector Quantization to predict the recurrence of the disease within 2 years or beyond and also within 5 years or beyond.
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Linawati, Linawati, Made Sudarma, and I. Putu Oka Wisnawa. "Forecasting rupiah exchange rate with learning vector quantization neural network." Indonesian Journal of Electrical Engineering and Computer Science 18, no. 1 (2020): 24. http://dx.doi.org/10.11591/ijeecs.v18.i1.pp24-31.

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&lt;p&gt;The classification technique and data forecasting will probably be one of the techniques that will often be needed in handling or managing big data. So, from that the author analyzes the possible development of the existing algorithms. The purpose is to find possibilities in the use of reliable algorithms in a particular field, then can be adopted and implemented to develop forecasting techniques in the future. Based on these considerations, the authors conducted experiments by applying LVQNN to conduct shortterm forecasting on daily period of the Rupiah exchange rate. The literature that is used as a reference is the discovery of architectural data classification processes that resemble forecasting techniques. So, when there is a combination of Rupiah exchange histories, it is possible to find these combinations into certain classes based on predetermined parameters and historical data combination data and forecast values in the past. In this research the factors chosen as indicators that affect the Rupiah exchange rate are the amount of exports, the amount of imports, the inflation rate and also the world oil price. In this research the highest accuracy value in the testing process for the population reached 99.0991%. The increase in the percentage value of forecasting accuracy is influenced by the composition of the data. In this study the formation of data composition is influenced by distinct data. The selection of parameters which become distinct claused determines how the composition of the data will be formed. If the composition of the data is not correct, the test results will not be good. If the number of weights vector is smaller than the input data, the forecasting accuracy will decrease. Because the weight vector cannot represent data combinations that used during training or testing.&lt;/p&gt;
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Devita, Retno, Ruri Hartika Zain, Hadi Syahputra, Evan Afri, and Intan Maulina. "Implementation and Development of Learning Vector Quantization Supervised Neural Network." Journal of Physics: Conference Series 2394, no. 1 (2022): 012009. http://dx.doi.org/10.1088/1742-6596/2394/1/012009.

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Abstract Electricity is vital energy for the sustainability of human activities both as individuals, community groups, and the industrial world. Electricity users are increasing every year, which causes irresponsible users and does not comply with existing rules; the number of users causes the staff to find it challenging to determine whether the power used is appropriate with household needs. This study uses data on 100 electricity users obtained from PT. PLN Rayon Trade is one of the branch offices of PLN in Indonesia. The method used to classify electricity users is the Learning Vector Quantization (LVQ) algorithm using the 4-8-3 architectural model. Several input variables are used, such as the number of bills, the number of hours, the metered rate, and the class. The results obtained an accuracy rate of 72% with a time of 11 minutes 53 seconds. So it can be concluded that the LVQ algorithm with the 4-8-3 architectural model can be used to classify electricity users. However, it is not very good because the accuracy still needs to be improved.
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Pham, D. T., and S. Sagiroglu. "Neural network classification of defects in veneer boards." Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture 214, no. 3 (2000): 255–58. http://dx.doi.org/10.1243/0954405001517649.

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Learning vector quantization (LVQ) networks are known good neural classifiers which provide fast and accurate results for many applications. The aim of this work was to test if this network paradigm could be employed for the classification of wood sheet defects. Experiments conducted with LVQ networks have shown that they provide a high degree of discrimination between the different types of defects and potentially can perform defect classification in real time.
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Hayat, Cynthia, and Iwan Aang Soenandi. "Hybrid Architecture Model of Genetic Algorithm and Learning Vector Quantization Neural Network for Early Identification of Ear, Nose, and Throat Diseases." Journal of Information Systems Engineering and Business Intelligence 10, no. 1 (2024): 1–12. http://dx.doi.org/10.20473/jisebi.10.1.1-12.

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Background: In 2020, the World Health Organization (WHO) estimated that 466 million people worldwide are affected by hearing loss, with 34 million of them being children. Indonesia is identified as one of the four Asian countries with a high prevalence of hearing loss, specifically at 4.6%. Previous research was conducted to identify diseases related to the Ear, Nose, and Throat, utilizing the certainty factor method with a test accuracy rate of 46.54%. The novelty of this research lies in the combination of two methods, the use of genetic algorithms for optimization and learning vector quantization to improve the level of accuracy for early identification of Ear, Nose, and Throat diseases. Objective: This research aims to produce a hybrid model between the genetic algorithm and the learning vector quantization neural network to be able to identify Ear, Nose, and Throat diseases with mild symptoms to improve accuracy. Methods: Implementing a 90:10 ratio means that 90% (186 data) of the data from the initial sequence is assigned for training purposes, while the remaining 10% (21 data) is allocated for testing. The procedural stages of genetic algorithm-learning vector quantization are population initialization, crossover, mutation, evaluation, selection elitism, and learning vector quantization training. Results The optimum hybrid genetic algorithm-learning vector quantization model for early identification of Ear, Nose, and Throat diseases was obtained with an accuracy of 82.12%. The parameter values with the population size 10, cr 0.9, mr 0.1, maximum epoch of 5000, error goal of 0.01, and learning rate (alpha) of 0.5. Better accuracy was obtained compared to backpropagation (64%), certainty factor 46.54%), and radial basic function (72%). Conclusion: Experiments in this research, successed identifying models by combining genetic algorithm-learning vector quantization to perform the early identification of Ear, Nose, and Throat diseases. For further research, it's very challenging to develop a model that automatically adapts the bandwidth parameters of the weighting functions during trainin Keywords: Early Identification, Ear-Nose-Throat Diseases, Genetic Algorithm, Learning Vector Quantization
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Tang, Pu Hua, Mu Rong Zhou, and Ying Yong Bu. "Classification of Underwater Echo Based on Fractal Theory and Learning Vector Quantization Neural Network." Applied Mechanics and Materials 148-149 (December 2011): 1365–69. http://dx.doi.org/10.4028/www.scientific.net/amm.148-149.1365.

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A classification method for underwater echo is introduced, which based on fractal theory and learning vector quantization (LVQ) neural network. The fractal dimension was extracted from the underwater echo by continuous wavelet transform. Combining with accumulative energy as input of a LVQ neural network, neural network was used to classify four kinds of underwater echo. The experimental results showed this method is effective and reliable.
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Aziz, Arafa Rahman, Budi Warsito, and Alan Prahutama. "PENGARUH TRANSFORMASI DATA PADA METODE LEARNING VECTOR QUANTIZATION TERHADAP AKURASI KLASIFIKASI DIAGNOSIS PENYAKIT JANTUNG." Jurnal Gaussian 10, no. 1 (2021): 21–30. http://dx.doi.org/10.14710/j.gauss.v10i1.30933.

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Learning Vector Quantization (LVQ) is a type of Artificial Neural Network with a supervised learning process based on competitive learning. Despite the absence of assumptions in LVQ is an advantage, it can be a problem when the predictor variables have big different ranges.This problems can be overcome by equalizing the range of all variables by data transformation so that all variables have relatively same effect. Heart Disease UCI dataset which used in this study is transformed by several transformation methods, such as minmax, decimal scaling, z-score, mean-MAD, sigmoid, and softmax. The result show that the six transformed data can provide better LVQ classification accuracy than the raw data which has 75.99% for training performance accuracy. LVQ classification accuracy with data transformation of minmax, decimal scaling, z-score, mean-MAD, sigmoid, and softmax are 89.16%, 88.22%, 89.7%, 90.1%, 88.17% and 92.18%. Based on the One-way ANOVA test and DMRT post hoc test known that there are significant differences between the results of the classification with data transformations and raw data in 0,05 significant level of α. It is also known that the best data transformation methods are softmax for training and sigmoid for testing. Keywords: heart disease, neural network, learning vector quantization, classification, data transformation
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Avdic, Senada, Roumiana Chakarova, and Imre Pazsit. "Analysis of the experimental positron lifetime spectra by neural networks." Nuclear Technology and Radiation Protection 18, no. 1 (2003): 16–21. http://dx.doi.org/10.2298/ntrp0301016a.

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This paper deals with the analysis of experimental positron lifetime spectra in polymer materials by using various algorithms of neural networks. A method based on the use of artificial neural networks for unfolding the mean lifetime and intensity of the spectral components of simulated positron lifetime spectra was previously suggested and tested on simulated data [Pzzsitetal, Applied Surface Science, 149 (1998), 97]. In this work, the applicability of the method to the analysis of experimental positron spectra has been verified in the case of spectra from polymer materials with three components. It has been demonstrated that the backpropagation neural network can determine the spectral parameters with a high accuracy and perform the decomposi-tion of lifetimes which differ by 10% or more. The backpropagation network has not been suitable for the identification of both the parameters and the number of spectral components. Therefore, a separate artificial neural network module has been designed to solve the classification problem. Module types based on self-organizing map and learning vector quantization algorithms have been tested. The learning vector quantization algorithm was found to have better performance and reliability. A complete artificial neural network analysis tool of positron lifetime spectra has been constructed to include a spectra classification module and parameter evaluation modules for spectra with a different number of components. In this way, both flexibility and high resolution can be achieved.
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AHN, Kyoung Kwan, and NGUYEN Huynh Thai Chau. "Force control of hybrid actuator using learning vector quantization neural network." Journal of Mechanical Science and Technology 20, no. 4 (2006): 447–54. http://dx.doi.org/10.1007/bf02916475.

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Rouza, Erni. "Prediksi Jenis Cacing Nematoda Usus Yang Menginfeksi Siswa Dengan Menggunakan Metoda LVQ." Digital Zone: Jurnal Teknologi Informasi dan Komunikasi 8, no. 2 (2017): 170–84. http://dx.doi.org/10.31849/digitalzone.v8i2.642.

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Abstrak-Pada saat ini, Jaringan Syaraf Tiruan (JST) telah banyak menjadi objek penelitian yang menarik, karena penerapannya sangat potensial dalam berbagai bidang sains, salah satu penerapannya didalam memprediksi penyakit. Penelitian ini bertujuan untuk mencoba menerapkan metode Learning vector Quantization (LVQ) dalam memprediksi jenis cacing Nematoda usus yang menginfeksi siswa dari nilai akurasi yang dihasilkan, karena beberapa penelitian menunjukkan bahwa anak usia sekolah dasar merupakan golongan yang sering terkena infeksi cacing usus. Dari hasil pelatihan dan pengujian menggunakan metode Learning Vector Quantization (LVQ) diketahui bahwa tingkat akurasi sesuai dengan hasil sebenarnya dan nilainya konstan, proses cepat hanya membutuhkan waktu paling lama 3 menit dan memberikan hasil yang optimal yaitu tingkat akurasi data latih sebesar 78,6885%, serta 80% untuk data uji. Hal ini menunjukkan bahwa jaringan yang terbentuk sudah cukup baik, akurat dan cepat dalam melakukan pembelajaran terhadap data input yang diberikan dalam memprediksi jenis cacing Nematoda Usus yang menginfeksi siswa. Kata kunci : Cacing Nematoda Usus, Jaringan Syaraf Tiruan, Learning Vector Quantization Abstract- At this time, an Artificial Neural Network (ANN) has been an interesting objects of research, because of application has potential in various fields of science, one application was used to predict diseases. This study aims to try to implement methods Learning vector quantization (LVQ) in predicting the type of Nematode worms that infect the intestines of students from the resulting accuracy value, because some studies show that children of primary school age are often exposed to a class of intestinal worm infections. From the results of the training and testing using methods Learning Vector Quantization (LVQ) note that the level of accuracy in accordance with the actual results and the value of the constant, quick process only takes a maximum of 3 minutes and provide optimal results is the level of training data accuracy of 78.6885%, and 80% for the test data. This indicates that the network is formed is quite good, accurate and fast in doing the learning on the input data given in predicting Intestinal Nematode worm species that infect students. Keywords: Intestinal Netamoda Worms, Artificial Neural Network, Learning Vector Quantization
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Waas, Chrisani, D. L. Rahakbauw, and Yopi Andry Lesnussa. "Cataract Disease Diagnosis System Using Artificial Neural Network Learning Vector Quantization Method." Journal of Applied Intelligent System 4, no. 2 (2020): 75–85. http://dx.doi.org/10.33633/jais.v4i2.3089.

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Artificial Neural Network (ANN) is an information processing system that has certain performance characteristics that are artificial representatives based on human neural networks. ANN method has been widely applied to help human performance, one of which is health. In this research, ANN will be used to diagnose cataracts, especially Congenital Cataracts, Juvenile Cataracts, Senile Cataracts and Traumatic Cataracts based on the symptoms of the disease. The ANN method used is the Learning Vector Quantization (LVQ) method. The data used in this research were 146 data taken from the medical record data of RSUD Dr. M. Haulussy, Ambon. The data consists of 109 data as training data and 37 data as testing data. By using learning rate (α) = 0.1, decrease in learning rate (dec α) = 0.0001 and maximum epoch (max epoch) = 5, the accuracy rate obtained is 100%.
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Arif, Alfis, Ferry Putrawansyah, Fitria Rahmadayanti, and Risnaini Masdalipa. "PENERAPAN NEURAL NETWORK MENGGUNAKAN METODE LEARNING QUANTIZATION UNTUK KLASIFIKASI UBI JALAR." Jusikom : Jurnal Sistem Komputer Musirawas 7, no. 2 (2022): 156–67. http://dx.doi.org/10.32767/jusikom.v7i2.1846.

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Tujuan dari penelitian ini adalah bertujuan untuk menghasilkan sistem Klasifikasi Jenis Ubi Jalar denganaMetode LearningaVectoraQuantization dengan Image Processing di Dinas Pertanian Kota Pagar Alam. Penelitian ini dilatar belakangi dengan proses pengklasifikasian jenis ubi jalar masih dilakukan secara konvensional dan belum terkomputerisasi, yakni pengklasifikasian ubi jalar masih berdasarkan pengalaman, warna dan bentuk dari ubi jalar. Halainiatentuasaja membutuhkanawaktu yangalama dan masih sering terjadi kesalahan, sehingga penelitian ini dapat membantu pengklasifikasian ubi jalaramenggunakan metodeaLearningaVectoraQuantization (LVQ) dengan cepat. aSistem yang dibangunamenggunakan Software MATLAB, dalam metode pengembangan sistem dalam penelitianaini adalah metodeaSDLC (Software Development Life Cycle), dimanaatahapan meliputiaanalisis, desain, pengkodean dan pengujian, untuk metode pengujian menggunakan Holdout Validation yang dibagi menjadi 2ayaitu dataatraining dan dataatesting. Hasiladariapenelitian ini yakni sistem Klasifikasi Jenis Ubi Jalar dengan metode Learning Vector Quantization dengan Image Processing dimana pada data training menghasilkan 69 data berhasil dan 11 data tidak berhasil sehingga mendapat persentase sebesar 86,25%. Kemudian setelah dilakukan holdout validation dengan 20 data testing menghasilkan 18 data berhasil dan 2 data tidak berhasil dengan persentase sebesar 90%. Akhirnya sistem yang menerapkan learning vector quantization terhadap klasifikasi jenis ubi jalar dengan image processing mendapat akurasi yang tinggi.
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DIAN, PRATIWI, SYAIFUDIN, RAHARDIANSYAH TRUBUS, ICHSAN GUNAWAN MUHAMAD, SEN STEVEN, and ADI PRATAMA DIMAS. "HANDWRITING PREDICTION WITH LEARNING VECTOR QUANTIZATION METHOD IN MOBILE APPLICATION." Journal of Theoretical and Applied Information Technology 100, no. 23 (2022): 5850–56. https://doi.org/10.5281/zenodo.7636221.

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Advances in technology are now increasing bringing people towards digital and mobile applications. To determine the owner of a handwriting, one of the manual techniques commonly used by humans that can be facilitated by mobile application technology is handwriting recognition. Learning Vector Quantization is one of the machine learning methods used to perform handwriting recognition and is one of the Artificial Neural Network (ANN) methods. This study aims to build a handwriting recognition system using the Learning Vector Quantization method on a mobile application, with feature extraction as the basic step in interpreting and classifying images. The results obtained from testing the prediction of learning vector quantization from 16 new data with a total of 80 tests. The results showed that 54 data were correct and 26 were incorrect, so the accuracy was 67.5%. Then obtained precision = 75.17%, recall = 67.5%, learning rate = 0.005, alpha value = 0.05 and iteration = 100
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Sela, Enny Itje. "Osteoporosis detection on the dental panoramic radiographic images using J48 algorithm and learning vector quantization." Jurnal Teknologi dan Sistem Komputer 9, no. 4 (2021): 211–17. http://dx.doi.org/10.14710/jtsiskom.2021.14197.

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Osteoporosis is one type of disease that is not easily detected. This disease can cause fractures for the sufferer. Early detection of osteoporosis is crucial to prevent fractures. This study aims to detect osteoporosis through features extracted from cortical bone and trabeculae in dental panoramic images. The results of the selected feature extraction are trained using an artificial neural network. Based on the study results, the dominant features for osteoporosis detection are radio morphometric index and morphological features. The accuracy, sensitivity, and specificity of the J48 and Learning Vector Quantization (LVQ) are 83.88 %, 78.57 %, and 100 %, respectively.
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Karayiannis, Nicolaos B. "Reformulating Learning Vector Quantization and Radial Basis Neural Networks." Fundamenta Informaticae 37, no. 1,2 (1999): 137–75. http://dx.doi.org/10.3233/fi-1999-371208.

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Uğuz, Harun. "Detection of Carotid Artery Disease by Using Learning Vector Quantization Neural Network." Journal of Medical Systems 36, no. 2 (2010): 533–40. http://dx.doi.org/10.1007/s10916-010-9498-8.

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Ahn, KyoungKwan, and ByungRyong Lee. "Intelligent switching control of pneumatic cylinders by learning vector quantization neural network." Journal of Mechanical Science and Technology 19, no. 2 (2005): 529–39. http://dx.doi.org/10.1007/bf02916175.

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Li, Robert, Earnest Sherrod, Jung Kim, and Gao Pan. "Fast image vector quantization using a modified competitive learning neural network approach." International Journal of Imaging Systems and Technology 8, no. 4 (1997): 413–18. http://dx.doi.org/10.1002/(sici)1098-1098(1997)8:4<413::aid-ima8>3.0.co;2-d.

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Tawakal, Firman, and Ahmedika Azkiya. "Diagnosa Penyakit Demam Berdarah Dengue (DBD) menggunakan Metode Learning Vector Quantization (LVQ)." JISKA (Jurnal Informatika Sunan Kalijaga) 4, no. 3 (2020): 56. http://dx.doi.org/10.14421/jiska.2020.43-07.

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Dengue Hemorrhagic Fever is a disease that is carried and transmitted through the mosquito Aedes aegypti and Aedes albopictus which is commonly found in tropical and subtropical regions such as in Indonesia to Northern Australia. in 2013 there are 2.35 million reported cases, which is 37,687 case is heavy cases of DHF. DHF’s symthoms have a similarity with typhoid fever, it often occur wrong handling. Therefore we need a system that is able to diagnose the disease suffered by patients, so that they can recognize whether the patient has DHF or Typhoid. The system will be built using Neural Network Learning Vector Quantization (LVQ) based on the best training results. This research is to diagnose Dengue Hemorrhagic Fever using LVQ with input parameters are hemoglobin, leukocytes, platelets, and heritrocytes. Based on result, the best accuracy is 97,14% with Mean Square Error (MSE) is 0.028571 with 84 train data and 36 test data. Conclution from the research is LVQ method can diagnose DHF Keywords: Dengue Hemorrhagic Fever; Learning Vector Quantization; classification; Neural Network;
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Pasaribu, Roni Fredy Halomoan, Muhammad Zarlis, and Erna Budhiarti Nababan. "Performance Level Analysis On Learning Vector Quantization And Cohonen Algorithms." sinkron 9, no. 1 (2025): 267–82. https://doi.org/10.33395/sinkron.v9i1.14313.

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Biometric identification is an alternative for a security system that consists of physiological characteristics and behavioral characteristics. Physiological characteristics are relatively stable physical characteristics such as fingerprints, hand lines, facial features, tooth patterns, and the retina of the eye. Behavioral characteristics such as signature, speech patterns, or typing rhythm. The function of a signature is proof in a document which states that the party signing, knows and agrees to all the contents of a document. There are several stages in the signature pattern image recognition system, namely the signature pattern image is produced through a scanning process, then the resulting digital signature image is cut (scaling) manually, the next process is thresholding, edge detection, image division, and representation. input value. The method used in recognizing signature patterns is the learning vector quantization (LVQ) artificial neural network method and kohonen self-organizing map (SOM). In Learning vector quantization, the initial weights are updated using existing patterns. Meanwhile, in the self-organizing map method, Kohonen takes initial weights randomly, then these weights are updated until they can classify themselves into the desired number of classes. The processes that occur in the artificial neural network method require a relatively long time. This is influenced by the large number of data samples used as a means of updating the trained weights. From the results of the research conducted, it shows that the learning rate value that was built around 0.2 &lt; α ≤ (10) ^ (-2) can produce better signature pattern recognition accuracy.
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Ariya Wibawa, Prayoga Bagas, and Aji Wibawa. "Review: Algoritma Klasifikasi pada Pengenalan Pola Citra." Jurnal Inovasi Teknologi dan Edukasi Teknik 2, no. 12 (2022): 557–65. http://dx.doi.org/10.17977/um068v2i122022p557-565.

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The classification algorithm is a technique in the field of machine learning and data mining that is used to group test data based on previously mapped training data. Pattern recognition can also be classified, but not all classification algorithms can perform pattern recognition. An algorithm or classification method that can perform image recognition is that in the Artificial Neural Network method there is a Learning Vector Quantization algorithm which has the advantage of being able to summarize the data set into small and the disadvantage is that it requires calculating all attributes and Fuzzy Neural Network which has, in Lazy Learner is K-Nearest Neighbor which has the advantage of being tough against noise and the disadvantage of needing to determine the value of k and the Template Matching method which is the simplest method, has high accuracy but has the disadvantage of large computational costs if the templates used are quite diverse. With the advantages and disadvantages of the methods previously mentioned, the researcher chose to compare the algorithms or methods of Learning Vector Quantization and Template Matching to be able to see how much accuracy and computational level to recognize an object of research. Algoritma klasifikasi adalah salah satu teknik pada bidang machine learning dan data mining yang digunakan untuk mengelompokkan data uji berdasarkan data latih yang sebelumnya sudah dipetakan. Pengenalan pola juga bisa di klasifikasikan tetapi tidak semua algoritma klasifikasi dapat melakukan pengenalan pola. Algoritma atau metode klasifikasi yang dapat melakukan pengenalan citra adalah pada metode Jaringan Syaraf Tiruan terdapat algoritma Learning Vector Quantization yang memiliki kelebihan mampu meringkas data set menjadi kecil dan kekurangan diperlukan perhitungan seluruh atribut dan Fuzzy Neural Network dimana mempunyai, pada Lazy Learner adalah K-Nearest Neighbor yang mempunyai kelebihan tangguh terhadap noise dan kekurangan perlu menentukan nilai k dan metode Template Matching yang merupakan metode paling simpel, memiliki akurasi yang tinggi tetapi memiliki kekurangan biaya komputasi yang besar jika template yang digunakan cukup beragam. Dengan kelebihan dan kekurangan dari metode – metode yang telah disebutkan sebelumnya, peneliti memilih membandingkan algoritma atau metode dari Learning Vector Quantization dan Template Matching untuk dapat melihat berapa besar akurasi dan tingkat komputasi untuk mengenali sebuah objek penelitian.
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Oktavius, Kevin, and Siska Devella. "Perbandingan Algoritma LVQ dan RBFNN Untuk Identifikasi Glaukoma dan Diabetes Retinopati Pada Citra Fundus." Jurnal Algoritme 1, no. 1 (2020): 68–77. http://dx.doi.org/10.35957/algoritme.v1i1.438.

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Penyakit mata merupakan salah satu masalah kesehatan utama pada semua orang terutama pada kaum lansia, penyakit mata yang paling umum menyerang lansia diantaranya adalah glaukoma dan retinopati diabetes. Penyakit glaukoma dan diabetes retinopati dapat diketahui melalui citra fundus. Pada penelitian ini telah dilakukan perbandingan algoritma Learning Vector Quantization dengan Radial Basis Function Neural Network untuk klasifikasi penyakit glaukoma dan diabetes retinopati (accuracy, precision, recall) berdasarkan citra fundus resolusi tinggi. Dataset yang digunakan berjumlah 45 citra fundus yang terdiri dari 15 citra fundus terjangkit glaukoma, 15 citra fundus terjangkit diabetes retinopati dan 15 citra fundus mata normal. Pada perhitungan dengan confusion matrix hasil tertinggi didapatkan pada algoritma radial basis function neural network dengan spread=20 dan MN=10 menghasilkan rata-rata accuracy sebesar 81,06%, precision sebesar 80,83% dan recall sebesar 73,33% jika dibandingkan dengan algoritma learning vector quantization dengan lvqnet=50 dan epoch=45 menghasilkan rata-rata accuracy sebesar 80,85%, precision sebesar 73,33% dan recall sebesar 77,14%.
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Zheng, Yuan, Xiaolan Ye, and Ting Wu. "Using an Optimized Learning Vector Quantization- (LVQ-) Based Neural Network in Accounting Fraud Recognition." Computational Intelligence and Neuroscience 2021 (June 28, 2021): 1–10. http://dx.doi.org/10.1155/2021/4113237.

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With the continuous development and wide application of artificial intelligence technology, artificial neural network technology has begun to be used in the field of fraud identification. Among them, learning vector quantization (LVQ) neural network is the most widely used in the field of fraud identification, and the fraud identification rate is relatively high. In this context, this paper explores this neural network technology in depth, uses the same fraud sample to test the fraud recognition rate of these two models, and proposes an optimized LVQ-based combined neural network fraud risk recognition model on this basis. This paper selects 550 listed companies that have committed fraud from 2015 to 2019 as the fraud samples, determines 550 nonfraud matching sample companies in accordance with the Beasley principle one-to-one, and uses this as the research sample. The fraud risk identification indicators with better identification effects combed out according to the literature were used as the initial indicator system. After the collinearity problem was eliminated through the paired sample T test and principal component analysis, the five indicators with the best identification effects were finally selected. Finally, based on the above theoretical analysis and empirical research summarizing the full text, it analyzes the shortcomings of this research and puts forward prospects for the future development of fraud risk identification models.
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Qu, Na, Jiatong Chen, Jiankai Zuo, and Jinhai Liu. "PSO–SOM Neural Network Algorithm for Series Arc Fault Detection." Advances in Mathematical Physics 2020 (January 25, 2020): 1–8. http://dx.doi.org/10.1155/2020/6721909.

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Self-organizing feature map (SOM) neural network is a kind of competitive neural network with unsupervised learning. It has the strong abilities of self-organization and self-learning. However, the classification accuracy of SOM neural network may decrease when the features of tested object are not obvious. In this paper, the particle swarm optimization (PSO) algorithm is used to optimize the weight values of SOM network. Three indexes, i.e., intra-class density, standard deviation and sample difference, are used to judge the weight value, which can improve the classification accuracy of the SOM network. PSO–SOM network is applied to the detection of series arc fault in electrical circuits and compared with conventional SOM network and learning vector quantization (LVQ) network. The detection accuracy of the PSO–SOM network is 95%, which is higher than conventional SOM network and LVQ network.
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Emperuman, Malathy, and Srimathi Chandrasekaran. "Hybrid Continuous Density Hmm-Based Ensemble Neural Networks for Sensor Fault Detection and Classification in Wireless Sensor Network." Sensors 20, no. 3 (2020): 745. http://dx.doi.org/10.3390/s20030745.

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Sensor devices in wireless sensor networks are vulnerable to faults during their operation in unmonitored and hazardous environments. Though various methods have been proposed by researchers to detect sensor faults, only very few research studies have reported on capturing the dynamics of the inherent states in sensor data during fault occurrence. The continuous density hidden Markov model (CDHMM) is proposed in this research to determine the dynamics of the state transitions due to fault occurrence, while neural networks are utilized to classify the faults based on the state transition probability density generated by the CDHMM. Therefore, this paper focuses on the fault detection and classification using the hybridization of CDHMM and various neural networks (NNs), namely the learning vector quantization, probabilistic neural network, adaptive probabilistic neural network, and radial basis function. The hybrid models of each NN are used for the classification of sensor faults, namely bias, drift, random, and spike. The proposed methods are evaluated using four performance metrics which includes detection accuracy, false positive rate, F1-score, and the Matthews correlation coefficient. The simulation results show that the learning vector quantization NN classifier outperforms the detection accuracy rate when compared to the other classifiers. In addition, an ensemble NN framework based on the hybrid CDHMM classifier is built with majority voting scheme for decision making and classification. The results of the hybrid CDHMM ensemble classifiers clearly indicates the efficacy of the proposed scheme in capturing the dynamics of change of statesm which is the vital aspect in determining rapidly-evolving instant faults that occur in wireless sensor networks.
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Lazarov, A., and C. Minchev. "ISAR Image Recognition Algorithm and Neural Network Implementation." Cybernetics and Information Technologies 17, no. 4 (2017): 183–99. http://dx.doi.org/10.1515/cait-2017-0048.

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AbstractThe image recognition and identification procedures are comparatively new in the scope of ISAR (Inverse Synthetic Aperture Radar) applications and based on specific defects in ISAR images, e.g., missing pixels and parts of the image induced by target’s aspect angles require preliminary image processing before identification. The present paper deals with ISAR image enhancement algorithms and neural network architecture for image recognition and target identification. First, stages of the image processing algorithms intended for image improving and contour line extraction are discussed. Second, an algorithm for target recognition is developed based on neural network architecture. Two Learning Vector Quantization (LVQ) neural networks are constructed in Matlab program environment. A training algorithm by teacher is applied. Final identification decision strategy is developed. Results of numerical experiments are presented.
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Chuan-Yu Chang and Da-Feng Zhuang. "A Fuzzy-Based Learning Vector Quantization Neural Network for Recurrent Nasal Papilloma Detection." IEEE Transactions on Circuits and Systems I: Regular Papers 54, no. 12 (2007): 2619–27. http://dx.doi.org/10.1109/tcsi.2007.906061.

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Somasundaram, Deepakrishna, Fangfang Zhang, Shenglei Wang, Huping Ye, Zongke Zhang, and Bing Zhang. "Learning vector quantization neural network for surface water extraction from Landsat OLI images." Journal of Applied Remote Sensing 14, no. 03 (2020): 1. http://dx.doi.org/10.1117/1.jrs.14.032605.

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Rahman, Agung Syaiful, Elvia Budianita, Reski Mai Candra, and Fadhilah Syafria. "Penerapan Learning Vector Quantization 3 Dalam Menentukan Bakat Anak." Jurnal Nasional Komputasi dan Teknologi Informasi (JNKTI) 5, no. 3 (2022): 408–14. http://dx.doi.org/10.32672/jnkti.v5i3.4398.

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Abstrak - Banyaknya bakat anak yang tidak diketahui oleh sebagian besar Orang tua di Indonesia dikarenakan sedikitnya ahli anak sebagai tempat untuk konsultasi yang menjadi faktor utama dalam perMasalahan ini. Tujuan dari penelitian ini ialah agar para Orang tua dapat mempermudah dalam menggali potensi dalam diri anak mereka masing-masing, yakni dengan menggunakan jaringan saraf tiruan. Ada beberapa metode dalam jaringan saraf tiruan, learning vector quantization 3 merupakan saah satu dari bagian tersebut. Bakat anak yang diambil merupakan bakat anak yang berdasarkan standar United State of Education America. Anak yang diteliti merupakan murid dari Sekolah Dasar Negeri 011 Titian Resak dengan rentang usia 10-12 tahun. Penelitian ini menunjukkan bahwa learning vector quantization 3 membutuhkan sedikitnya 5 kriteria dengan 30 variabel bakat anak sebagai dasar dari penelitian ini. Berdasarkan hasil yang didapatkan, sistem ini berhasil mengidentifikasi bakat anak dengan rentang pembagian 90% data latih dan 10% data uji dan parameter window (0.1,0.2,0.3), epsilon (0.1,0.2,0.3), alpha (0.1) sebesar 81.82%.Kata kunci : Bakat Anak, Learning Vector Quantization 3, Jaringan Saraf Tiruan Abstract - The number of children's taents that are not known by most parents in Indonesia is due to the lack of child experts as a place for consultation which is the main factor in this problem. The purpose of this research is that parents can make it easier to explore the potentia in their respective children, namely by using artificia neura networks. There are severa methods in artificia neura networks, learning vector quantization 3 is one of them. The taent of the child taken is the child's taent based on the standards of the United State of Education America. The children studied were students from the 011 Titian Resak State Elementary School with an age range of 10-12 years. This study shows that learning vector quantization 3 requires at least 5 criteria with 30 variables of children's taents as the basis of this research. Based on the results obtained, this system succeeded in identifying children's taents with a distribution range of 90% of training data and 10% of test data and parameters window (0.1.0.2.0.3), epsilon (0.1.0.2.0.3), apha (0.1) of 81.82% .Keyword : Child Talent, Learning Vector Quantization 3, Artificia Neura Network
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Pham, D. T., and E. Oztemel. "Control Chart Pattern Recognition Using Combinations of Multi-Layer Perceptrons and Learning-Vector-Quantization Neural Networks." Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering 207, no. 2 (1993): 113–18. http://dx.doi.org/10.1243/pime_proc_1993_207_325_02.

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Pattern recognition systems made up of independent multi-layer perceptrons and learning-vector-quantization neural network modules have been developed for classifying control chart patterns. These composite pattern recognition systems have better classification capabilities than their individual modules. The paper describes the structures of these pattern recognition systems and the results obtained on using them.
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Sarhan, Shahenda, Aida A. Nasr, and Mahmoud Y. Shams. "Multipose Face Recognition-Based Combined Adaptive Deep Learning Vector Quantization." Computational Intelligence and Neuroscience 2020 (September 24, 2020): 1–11. http://dx.doi.org/10.1155/2020/8821868.

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Multipose face recognition system is one of the recent challenges faced by the researchers interested in security applications. Different researches have been introduced discussing the accuracy improvement of multipose face recognition through enhancing the face detector as Viola-Jones, Real Adaboost, and Cascade Object Detector while others concentrated on the recognition systems as support vector machine and deep convolution neural networks. In this paper, a combined adaptive deep learning vector quantization (CADLVQ) classifier is proposed. The proposed classifier has boosted the weakness of the adaptive deep learning vector quantization classifiers through using the majority voting algorithm with the speeded up robust feature extractor. Experimental results indicate that, the proposed classifier provided promising results in terms of sensitivity, specificity, precision, and accuracy compared to recent approaches in deep learning, statistical, and classical neural networks. Finally, the comparison is empirically performed using confusion matrix to ensure the reliability and robustness of the proposed system compared to the state-of art.
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QIAO, Hui, Yan-zhou ZHOU, and Nan SHAO. "Software reliability prediction based on learning vector quantization neutral network." Journal of Computer Applications 32, no. 5 (2013): 1436–38. http://dx.doi.org/10.3724/sp.j.1087.2012.01436.

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